Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	133,710
2	South Dakota	131,237
3	Rhode Island	126,808
4	Utah	119,206
5	Tennessee	115,760
6	Arizona	115,026
7	Oklahoma	110,092
8	Iowa	109,976
9	Arkansas	109,001
10	Wisconsin	108,209
11	Nebraska	107,216
12	South Carolina	105,446
13	Alabama	104,378
14	Kansas	103,890
15	Mississippi	101,889
16	Indiana	101,357
17	Idaho	99,746
18	New Jersey	98,381
19	Nevada	97,890
20	Illinois	96,940
21	Montana	96,913
22	Wyoming	96,411
23	Georgia	96,367
24	Texas	95,280
25	Kentucky	95,260
26	Delaware	95,104
27	Louisiana	95,029
28	Missouri	94,439
29	Florida	93,888
30	California	92,326
31	New York	92,257
32	New Mexico	90,643
33	Minnesota	89,952
34	Massachusetts	89,618
35	North Carolina	86,251
36	Ohio	85,791
37	Connecticut	84,303
38	Alaska	83,674
39	Colorado	78,832
40	Pennsylvania	77,897
41	West Virginia	77,241
42	Virginia	71,142
43	Michigan	70,226
44	Maryland	66,429
45	District of Columbia	61,619
46	New Hampshire	59,668
47	Washington	47,182
48	Puerto Rico	43,367
49	Oregon	38,433
50	Maine	36,283
51	Vermont	28,739
52	Hawaii	20,446

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	420
2	Rhode Island	408
3	Connecticut	362
4	Michigan	347
5	Tennessee	308
6	New York	264
7	Alaska	241
8	Massachusetts	233
9	Delaware	228
10	Pennsylvania	205
11	Vermont	201
12	New Hampshire	200
13	Florida	188
14	West Virginia	176
15	Minnesota	174
16	Montana	167
17	South Carolina	163
18	District of Columbia	147
19	Louisiana	145
20	South Dakota	142
21	Iowa	141
22	Idaho	140
23	Georgia	136
24	North Carolina	136
25	Virginia	136
26	Maryland	132
27	Wyoming	125
28	Nebraska	123
29	Illinois	119
30	Kentucky	119
31	Maine	119
32	Texas	116
33	North Dakota	115
34	Ohio	114
35	Colorado	104
36	Indiana	96
37	Oklahoma	96
38	Utah	95
39	Washington	83
40	New Mexico	80
41	Missouri	73
42	Puerto Rico	69
43	Arizona	67
44	Mississippi	62
45	California	61
46	Nevada	59
47	Oregon	59
48	Kansas	50
49	Wisconsin	49
50	Alabama	47
51	Arkansas	44
52	Hawaii	44

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,729
2	New York	2,517
3	Rhode Island	2,459
4	Massachusetts	2,454
5	Mississippi	2,344
6	Arizona	2,307
7	Connecticut	2,200
8	South Dakota	2,173
9	Louisiana	2,159
10	Alabama	2,131
11	North Dakota	1,960
12	Pennsylvania	1,943
13	Indiana	1,924
14	New Mexico	1,862
15	Illinois	1,845
16	Arkansas	1,838
17	Iowa	1,801
18	South Carolina	1,753
19	Tennessee	1,704
20	Georgia	1,697
21	Michigan	1,694
22	Nevada	1,684
23	Kansas	1,664
24	Texas	1,645
25	Delaware	1,576
26	Ohio	1,572
27	Florida	1,528
28	District of Columbia	1,489
29	California	1,465
30	West Virginia	1,458
31	Missouri	1,447
32	Maryland	1,355
33	Montana	1,327
34	Kentucky	1,312
35	Wisconsin	1,243
36	Minnesota	1,215
37	Oklahoma	1,210
38	Wyoming	1,200
39	Virginia	1,187
40	Nebraska	1,160
41	North Carolina	1,132
42	Idaho	1,093
43	Colorado	1,067
44	New Hampshire	895
45	Washington	689
46	Puerto Rico	657
47	Utah	647
48	Oregon	564
49	Maine	543
50	Alaska	407
51	Vermont	352
52	Hawaii	318

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	6
2	Massachusetts	4
3	New Jersey	4
4	California	3
5	Delaware	3
6	Louisiana	3
7	Missouri	3
8	Rhode Island	3
9	South Carolina	3
10	Arizona	2
11	Maryland	2
12	Mississippi	2
13	New Mexico	2
14	Texas	2
15	West Virginia	2
16	Arkansas	1
17	Connecticut	1
18	Florida	1
19	Georgia	1
20	Idaho	1
21	Illinois	1
22	Indiana	1
23	Montana	1
24	Nevada	1
25	New York	1
26	Ohio	1
27	Tennessee	1
28	Utah	1
29	Virginia	1
30	Washington	1
31	Wyoming	1
32	Alabama	0
33	Alaska	0
34	Colorado	0
35	District of Columbia	0
36	Hawaii	0
37	Iowa	0
38	Kansas	0
39	Maine	0
40	Michigan	0
41	Minnesota	0
42	Nebraska	0
43	New Hampshire	0
44	North Carolina	0
45	North Dakota	0
46	Oklahoma	0
47	Oregon	0
48	Pennsylvania	0
49	Puerto Rico	0
50	South Dakota	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	350,437	1	99
Chattahoochee	Georgia	314,385	2	99
Bent	Colorado	266,093	3	99
Dewey	South Dakota	243,551	4	99
Lincoln	Arkansas	243,550	5	99
Davidson	Tennessee	134,299	210	93
Richland	South Carolina	104,058	1042	66
York	South Carolina	100,232	1215	61
Orange	California	83,443	1996	36
Pierce	Washington	45,532	2905	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Foard	Texas	7,792	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,460	1789	43
Davidson	Tennessee	1,274	2004	36
Richland	South Carolina	1,258	2028	35
York	South Carolina	1,239	2063	34
Pierce	Washington	660	2720	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.